Googling Stroke ASPECTS to determine disability

Exploratory analysis from vista-acute collaboration

Richard Beare, Jian Chen, Thanh G. Phan, Kennedy R. Lees, Myzoon Ali, Andrei Alexandrov, P. M. Bath, E. Bluhmki, N. Bornstein, L. Claesson, S. M. Davis, G. Donnan, H. C. Diener, M. Fisher, M. Ginsberg, B. Gregson, J. Grotta, W. Hacke, M. G. Hennerici, M. Hommel & 10 others M. Kaste, P. Lyden, J. Marler, K. Muir, R. Sacco, A. Shuaib, P. Teal, N. G. Wahlgren, S. Warach, C. Weimar

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The summed Alberta Stroke Program Early CT Score (ASPECTS) is useful for predicting stroke outcome. The anatomical information in the CT template is rarely used for this purpose because traditional regression methods are not adept at handling collinearity (relatedness) among brain regions. While penalized logistic regression (PLR) can handle collinearity, it does not provide an intuitive understanding of the interaction among network structures in a way that eigenvector method such as PageRank can (used in Google search engine). In this exploratory analysis we applied graph theoretical analysis to explore the relationship among ASPECTS regions with respect to disability outcome. The Virtual International Stroke Trials Archive (VISTA) was searched for patients who had infarct in at least one ASPECTS region (ASPECTS ≤9, ASPECTS=10 were excluded), and disability (modified Rankin score/mRS). A directed graph was created from a cross correlation matrix (thresholded at false discovery rate of 0.01) of the ASPECTS regions and demographic variables and disability (mRS>2). We estimated the network-based importance of each ASPECTS region by comparing PageRank and node strength measures. These results were compared with those from PLR. There were 185 subjects, average age 67.5± 12.8 years (55% Males). Model 1: demographic variables having no direct connection with disability, the highest PageRank was M2 (0.225, bootstrap 95% CI 0.215-0.347). Model 2: demographic variables having direct connection with disability, the highest PageRank were M2 (0.205, bootstrap 95% CI 0.194-0.367) and M5 (0.125, bootstrap 95% CI 0.096-0.204). Both models illustrate the importance of M2 region to disability. The PageRank method reveals complex interaction among ASPECTS regions with respects to disability. This approach may help to understand the infarcted brain network involved in stroke disability.

Original languageEnglish (US)
Article numbere0125687
JournalPloS one
Volume10
Issue number5
DOIs
StatePublished - May 11 2015

Fingerprint

Alberta
stroke
Stroke
Logistics
Brain
Directed graphs
Search engines
Eigenvalues and eigenfunctions
demographic statistics
Demography
Logistic Models
brain
Search Engine
infarction
engines
methodology

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Googling Stroke ASPECTS to determine disability : Exploratory analysis from vista-acute collaboration. / Beare, Richard; Chen, Jian; Phan, Thanh G.; Lees, Kennedy R.; Ali, Myzoon; Alexandrov, Andrei; Bath, P. M.; Bluhmki, E.; Bornstein, N.; Claesson, L.; Davis, S. M.; Donnan, G.; Diener, H. C.; Fisher, M.; Ginsberg, M.; Gregson, B.; Grotta, J.; Hacke, W.; Hennerici, M. G.; Hommel, M.; Kaste, M.; Lyden, P.; Marler, J.; Muir, K.; Sacco, R.; Shuaib, A.; Teal, P.; Wahlgren, N. G.; Warach, S.; Weimar, C.

In: PloS one, Vol. 10, No. 5, e0125687, 11.05.2015.

Research output: Contribution to journalArticle

Beare, R, Chen, J, Phan, TG, Lees, KR, Ali, M, Alexandrov, A, Bath, PM, Bluhmki, E, Bornstein, N, Claesson, L, Davis, SM, Donnan, G, Diener, HC, Fisher, M, Ginsberg, M, Gregson, B, Grotta, J, Hacke, W, Hennerici, MG, Hommel, M, Kaste, M, Lyden, P, Marler, J, Muir, K, Sacco, R, Shuaib, A, Teal, P, Wahlgren, NG, Warach, S & Weimar, C 2015, 'Googling Stroke ASPECTS to determine disability: Exploratory analysis from vista-acute collaboration', PloS one, vol. 10, no. 5, e0125687. https://doi.org/10.1371/journal.pone.0125687
Beare, Richard ; Chen, Jian ; Phan, Thanh G. ; Lees, Kennedy R. ; Ali, Myzoon ; Alexandrov, Andrei ; Bath, P. M. ; Bluhmki, E. ; Bornstein, N. ; Claesson, L. ; Davis, S. M. ; Donnan, G. ; Diener, H. C. ; Fisher, M. ; Ginsberg, M. ; Gregson, B. ; Grotta, J. ; Hacke, W. ; Hennerici, M. G. ; Hommel, M. ; Kaste, M. ; Lyden, P. ; Marler, J. ; Muir, K. ; Sacco, R. ; Shuaib, A. ; Teal, P. ; Wahlgren, N. G. ; Warach, S. ; Weimar, C. / Googling Stroke ASPECTS to determine disability : Exploratory analysis from vista-acute collaboration. In: PloS one. 2015 ; Vol. 10, No. 5.
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abstract = "The summed Alberta Stroke Program Early CT Score (ASPECTS) is useful for predicting stroke outcome. The anatomical information in the CT template is rarely used for this purpose because traditional regression methods are not adept at handling collinearity (relatedness) among brain regions. While penalized logistic regression (PLR) can handle collinearity, it does not provide an intuitive understanding of the interaction among network structures in a way that eigenvector method such as PageRank can (used in Google search engine). In this exploratory analysis we applied graph theoretical analysis to explore the relationship among ASPECTS regions with respect to disability outcome. The Virtual International Stroke Trials Archive (VISTA) was searched for patients who had infarct in at least one ASPECTS region (ASPECTS ≤9, ASPECTS=10 were excluded), and disability (modified Rankin score/mRS). A directed graph was created from a cross correlation matrix (thresholded at false discovery rate of 0.01) of the ASPECTS regions and demographic variables and disability (mRS>2). We estimated the network-based importance of each ASPECTS region by comparing PageRank and node strength measures. These results were compared with those from PLR. There were 185 subjects, average age 67.5± 12.8 years (55{\%} Males). Model 1: demographic variables having no direct connection with disability, the highest PageRank was M2 (0.225, bootstrap 95{\%} CI 0.215-0.347). Model 2: demographic variables having direct connection with disability, the highest PageRank were M2 (0.205, bootstrap 95{\%} CI 0.194-0.367) and M5 (0.125, bootstrap 95{\%} CI 0.096-0.204). Both models illustrate the importance of M2 region to disability. The PageRank method reveals complex interaction among ASPECTS regions with respects to disability. This approach may help to understand the infarcted brain network involved in stroke disability.",
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AU - Ali, Myzoon

AU - Alexandrov, Andrei

AU - Bath, P. M.

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AU - Bornstein, N.

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AU - Davis, S. M.

AU - Donnan, G.

AU - Diener, H. C.

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AU - Hennerici, M. G.

AU - Hommel, M.

AU - Kaste, M.

AU - Lyden, P.

AU - Marler, J.

AU - Muir, K.

AU - Sacco, R.

AU - Shuaib, A.

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